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Computational Phenotyping
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Computational Phenotyping is the process of transforming the noisy, massive Electronic Health Record (EHR) data into meaningful medical concepts that can be used to predict the risk of disease for an individual, or the response to drug therapy.
Source: Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Supervised Coupled Matrix-Tensor Factorization (SCMTF) for Computational Phenotyping of Patient Reported Outcomes in Ulcerative Colitis
SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
M3H: Multimodal Multitask Machine Learning for Healthcare
Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Benchmarking machine learning models on multi-centre eICU critical care dataset
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer
Natural Language Processing for EHR-Based Computational Phenotyping
Federated Tensor Factorization for Computational Phenotyping
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Multitask learning and benchmarking with clinical time series data
Unsupervised Learning for Computational Phenotyping
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain
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